Evaluating Human Eye Features for Objective Measure of Working Memory Capacity

ABSTRACT

Eye tracking measures can provide means to understand the underlying development of human working memory. In this study, we propose to develop machine learning algorithms to find an objective relationship between human eye movements via oculomotor plant and their working memory capacity, which determines subjective cognitive load. Here we evaluate oculomotor plant features extracted from saccadic eye movements, traditional positional gaze metrics, and advanced eye metrics such as ambient/focal coefficient , gaze transition entropy, low/high index of pupillary activity (LHIPA), and real-time index of pupillary activity (RIPA). This paper outlines the proposed approach of evaluating eye movements for obtaining an objective measure of the working memory capacity and a study to investigate how working memory capacity is affected when reading AI-generated fake news.

References

1. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
2. Andrew T Duchowski. 2017. The Gaze Analytics Pipeline. In Eye Tracking Methodology. Springer Publishing, New York, NY, USA, 175–191.
3. Andrew T. Duchowski, Krzysztof Krejtz, Nina A. Gehrer, Tanya Bafna, and Per Bækgaard. 2020. The Low/High Index of Pupillary Activity. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3313831.3376394
4. Andrew T. Duchowski, Krzysztof Krejtz, Izabela Krejtz, Cezary Biele, Anna Niedzielska, Peter Kiefer, Martin Raubal, and Ioannis Giannopoulos. 2018. The Index of Pupillary Activity: Measuring Cognitive Load Vis-à-Vis Task Difficulty with Pupil Oscillation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3173856
5. Gavindya Jayawardena. 2020. RAEMAP: Real-Time Advanced Eye Movements Analysis Pipeline. In Proceedings of the Symposium on Eye Tracking Research and Applications(ETRA). Association for Computing Machinery, New York, NY, USA, 1–4.
6. Gavindya Jayawardena, Yasith Jayawardana, Sampath Jayarathna, Jonas Högström, Thomas Papa, Deepak Akkil, Andrew T Duchowski, Vsevolod Peysakhovich, Izabela Krejtz, Nina Gehrer, 2022. Toward a Real-Time Index of Pupillary Activity as an Indicator of Cognitive Load. Procedia Computer Science 207 (2022), 1331–1340.
7. Gavindya Jayawardena, Anne Michalek, Andrew Duchowski, and Sampath Jayarathna. 2020. Pilot study of audiovisual speech-in-noise (sin) performance of young adults with adhd. In Proceedings of the Symposium on Eye Tracking Research and Applications(ETRA). Association for Computing Machinery, New York, NY, USA, 1–5.
8. Krzysztof Krejtz, Andrew Duchowski, Izabela Krejtz, Agnieszka Szarkowska, and Agata Kopacz. 2016. Discerning Ambient/Focal Attention with Coefficient K. ACM Transactions on Applied Perception 13, 3, Article 11 (may 2016), 20 pages. https://doi.org/10.1145/2896452
9. Krzysztof Krejtz, Andrew Duchowski, Tomasz Szmidt, Izabela Krejtz, Fernando González Perilli, Ana Pires, Anna Vilaro, and Natalia Villalobos. 2015. Gaze Transition Entropy. ACM Transactions on Applied Perception 13, 1, Article 4 (2015), 20 pages. https://doi.org/10.1145/2834121
10. Krzysztof Krejtz, Andrew T Duchowski, Anna Niedzielska, Cezary Biele, and Izabela Krejtz. 2018. Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PloS one 13, 9 (2018), e0203629.
11. Krzysztof Krejtz, Tomasz Szmidt, Andrew T Duchowski, and Izabela Krejtz. 2014. Entropy-based statistical analysis of eye movement transitions. In Proceedings of the Symposium on Eye Tracking Research and Applications. Association for Computing Machinery, New York, NY, USA, 159–166.
12. Sarah Kreps, R Miles McCain, and Miles Brundage. 2022. All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation. Journal of experimental political science 9, 1 (2022), 104–117.
13. Priyanka Ranade, Anupam Joshi, and Tim Finin. 2021. Study shows AI-generated fake reports fool experts. UMBC Computer Science and Electrical Engineering Department Collection (2021).
14. Sobiga Shanmugathasan and Sampath Jayarathna. 2018. Oculomotor Plant Feature Extraction from Human Saccadic Eye Movements. In 2018 IEEE International Conference on Information Reuse and Integration (IRI). 130–133. https://doi.org/10.1109/IRI.2018.00027
15. Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, 2022. Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv preprint arXiv:2201.11990 (2022).

Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

    • Empirical studies in HCI